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uniformize w/ the cache_dir update

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VictorSanh
2019-11-05 10:57:24 -05:00
committed by Lysandre Debut
parent d75d49a51d
commit abd397e954
1 changed files with 11 additions and 3 deletions
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14
examples/run_xnli.py
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@@ -467,9 +467,17 @@ def main():
args.model_type = args.model_type.lower() args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name) config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case) num_labels=num_labels,
model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config) finetuning_task=args.task_name,
cache_dir=args.cache_dir if args.cache_dir else None)
tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None)
model = model_class.from_pretrained(args.model_name_or_path,
from_tf=bool('.ckpt' in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None)
if args.local_rank == 0: if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab